The file dataset.py contains the letters A through Z as 5-by-7 dot-matrix fonts, in a format similar to the Hebb Net example for character recognition. There are two different fonts, one for training and one for test.
NumPy is to implement vector operations such as dot product, and Matplotlib library to generate plots.
There are 26 perceptrons in total, one for each letter. Each perceptron learns to output 1 for its assigned letter and 0 for all other letters. Weights and biases are initialized to random values (rather than zero) for each perceptron, following which the perceptron learning algorithm is applied until all items in the training set are classified correctly or until it becomes clear that the weights will not converge.
The output of each perceptron, which classifies an item in the training set, is recorded for correctness. At the end of each pass through the training set, the error rate (number of misclassified items) of that perceptron is also recorded for that epoch.
If a letter is linearly separable from the others and the learning rate is small enough, there is a downward trend in error rate until each point is classified correctly. matplotlib is used to plot the error rate as a function of the number of epochs in order to visualize this trend.
Once the perceptrons have been trained (in other words, once the perceptrons for the letters which are linearly separable from other letters have converged), each trained perceptron is tested against the letters in the test set.